Learning Spaces Research Articles

Video-Based Sign Language Recognition via ResNet and LSTM Network.

Sign language recognition technology can help people with hearing impairments to communicate with non-hearing-impaired people. At present, with the rapid development of society, deep learning also provides certain technical support for sign language recognition work. In sign language recognition tasks, traditional convolutional neural networks used to extract spatio-temporal features from sign language videos suffer from insufficient feature extraction, resulting in low recognition rates. Nevertheless, a large number of video-based sign language datasets require a significant amount of computing resources for training while ensuring the generalization of the network, which poses a challenge for recognition. In this paper, we present a video-based sign language recognition method based on Residual Network (ResNet) and Long Short-Term Memory (LSTM). As the number of network layers increases, the ResNet network can effectively solve the granularity explosion problem and obtain better time series features. We use the ResNet convolutional network as the backbone model. LSTM utilizes the concept of gates to control unit states and update the output feature values of sequences. ResNet extracts the sign language features. Then, the learned feature space is used as the input of the LSTM network to obtain long sequence features. It can effectively extract the spatio-temporal features in sign language videos and improve the recognition rate of sign language actions. An extensive experimental evaluation demonstrates the effectiveness and superior performance of the proposed method, with an accuracy of 85.26%, F1-score of 84.98%, and precision of 87.77% on Argentine Sign Language (LSA64).

Student choice and barriers to physical activity in Latino boys in the REACH after-school program

ABSTRACT Background After-school programs (ASP) provide safe and enjoyable setting for youth to participate in sports and physical activities (PA). The flexibility in the structure of ASPs can facilitate non-traditional approaches to teaching PA which consider the context of the community, students’ backgrounds, and focuses on positive youth development. REACH takes a non-traditional approach that aims to provide youth in under-resourced communities with a safe space for PA and learning through a sport-centered curriculum. Purpose The objective was to highlight the perspectives of Latino boys regarding their lived experiences in an after-school soccer and literacy program in a diverse urban school. The research questions were: (a) what are the lived experiences of young boys in a year-long after-school program in a diverse urban setting? (b) What opportunities did REACH provide that helped the young boys negotiate barriers to being physically active? Method Twenty-three Latino boys participated in the REACH after-school sessions over a school year. Four pre-service teachers facilitated soccer sessions at REACH. Data were collected through boys’ weekly journals across 28 weeks and interviews with ten boys at the end of the year. Coaches’ reflexive journals were used to triangulate the data. Data were independently coded using constant comparison and analyzed following a collaborative qualitative analysis procedure. Trustworthiness included triangulation of data, negative cases checks, multiple coders, and peer-debriefing. Results The results presented in this article are part of a larger year-long mixed-methods curriculum intervention. Two themes are presented to represent the perspectives of the boys in participating in REACH. In the first theme, we discuss student choice that was expressed in the way youth chose to participate in REACH and engage in PA during out-of-school hours. The second theme presents the students’ identifying the barriers they face to being physically active and the ways REACH provided a space for them to reflect on those barriers. Discussion Students’ choice was expressed in participants’ engagement in physical activities and sports in and outside of REACH and as a fundamental aspect that enhanced enjoyment in the program. Thus, participating in the program led students to enhance their physical activity enjoyment in and out of the program as well as learn about life skills through sport. With a focus on positive youth development, discussions and journaling in REACH encouraged the boys to reflect on life skills that focused on character and the development of positive relationships. Participants described varied barriers they faced to being physically active in and out of schools. REACH provided a safe space where participants could talk about and develop awareness about aspects of their community such as violence. Conclusion REACH provided a safe space for students to be active, learn sports skills, and engage in interest-based activities. The safe space provided in REACH also encouraged Latino boys to reflect on the barriers to being physically active outside of the program with the intent of possibly transferring what was learned in the program to their lives. Having a choice was a fundamental aspect to enhancing the boys’ enjoyment in REACH.

Seeing mathematics together: A comparative case study of youths and facilitators collaborating to learn mathematics in informal settings

BackgroundThis comparative case study examined the use of math walks with middle grade youths and adult facilitators in an informal STEM learning space. Math walks are place-based walking tours where youths and facilitators critically examine and ask math-related questions about their environment. MethodDrawing on situated theories of learning and frameworks for understanding group participation, we examined how facilitators constrained or supported youths’ mathematical thinking as they participated in math walks at the local zoo. ResultsUsing interaction and stance analysis, we identified, analyzed, and compared three contrasting cases: In the first case, the facilitator may have overly constrained youths’ mathematical thinking by asking leading questions and not providing time for youths to discuss their personal interests. In the second case, the facilitator may have underly constrained youths’ mathematical thinking by allowing youths to ask too many new questions without refining or developing any one specific question. In the third case, the facilitator supported mathematical thinking by praising youths’ work, layering on mathematical terminology, and providing clear and actionable instructions for how youths could refine their mathematical questions. ConclusionsFindings support efforts to understand how adult facilitators can support youths in seeing mathematics within and asking mathematical questions about the world around them.

Changes to Secondary School Physical Activity Programs and Policy after Emerging from COVID-19 Lockdowns.

The purpose of this study was to explore the adaptations that schools made to physical activity programs and facilities, and disparities by area urbanicity and income, during the first school year after the emergence of the COVID-19 pandemic. In a convenience sample of 132 secondary schools in Canada, school contacts responded to an annual survey in the 2020-2021 school year on changes to physical activity programs and facilities, and related staff training. Content analysis categorized open-ended text responses, and schools were compared based on area urbanicity and median income. Most schools canceled all interschool sports (88.9%) and intramurals (65.9%). New programs were added by 12.6% of schools, and about half (49%) of schools reported some continuing programs, most of which were sports programs, followed by facility and equipment access. Physical activity facilities were closed in 18.1% of schools, while 15.7% had new facilities added, and 11% temporarily converted facilities into learning spaces. Large/medium urban schools were at greater odds of having made any change to their facilities compared to schools in rural/small urban areas (odds ratio (95% confidence interval): 2.3 (1.1, 4.8)). The results demonstrate the considerable scale and nature of the restrictions in school provisions of physical activity opportunities during this period, as well as the resourcefulness of some schools in adding new programs and facilities.

Local Contrast Learning for One-Shot Learning

Learning a deep model from small data is an opening and challenging problem. In high-dimensional spaces, few samples only occupy an extremely small portion of the space, often exhibiting sparsity issues. Classifying in this globally sparse sample space poses significant challenges. However, by using a single sample category as a reference object for comparing and recognizing other samples, it is possible to construct a local space. Conducting contrastive learning in this local space can overcome the sparsity issue of a few samples. Based on this insight, we proposed a novel deep learning approach named Local Contrast Learning (LCL). This is analogous to a key insight into human cognitive behavior, where humans identify the objects in a specific context by contrasting them with the objects in that context or from their memory. LCL is used to train a deep model that can contrast the recognized sample with a couple of contrastive samples that are randomly drawn and shuffled. On a one-shot classification task on Omniglot, the deep model-based LCL with 86 layers and 1.94 million parameters, which was trained on a tiny dataset with only 60 classes and 20 samples per class, achieved an accuracy of 98.95%. Furthermore, it achieved an accuracy of 99.24% at 156 classes and 20 samples per class. LCL is a fundamental idea that can be applied to alleviate the parametric model’s overfitting resulting from a lack of training samples.

Initial teacher education seminars and minority ethnic students: exploring dialogic teaching and engagement

ABSTRACT This small-scale action research acts on the calls for more inclusive and holistic higher education. It explores the impact of dialogic teaching approaches on student engagement, focusing on the experiences of initial teacher education students from ethnic minority backgrounds. The qualitative analysis reveals how these practices increase student engagement and contribute to better student experiences. Dialogic teaching cultivates safe learning spaces and empowers students to contribute more freely during seminars, improving their social, emotional, and personal engagement with learning. The students included in this study also perceived dialogic teaching practices as improving their experiences by supporting them in forming relationships. However, the analysis suggests that they do not fully remove all barriers to learning and engagement. Therefore, further research is needed to examine both the benefits and complexities of dialogic pedagogies in HE and the extent to which these practices contribute to fostering inclusive spaces within multicultural university settings.

Pedagogy, place, and food education in Australian schools: lessons from Tropical North Queensland

ABSTRACT Children today have limited food origin awareness. The further we move away from food production practices, the less exposure children have to them, and the more food vulnerable we become. This is especially true for children growing up in urban areas where there is limited space for food gardens. Schools have developed targeted pedagogical approaches to raise food origin awareness, and this paper examines one such attempt in Cairns, Australia. We compare how students aged 5-6 responded to an activity where they drew their immediate response to the word ‘food'. Comparisons were then made between those learning under explicit instruction and those using a more experiential, place-based pedagogical approach. The findings suggest students in the experiential class who regularly use the garden as a learning space drew significantly higher levels of fresh, place-appropriate fruits and vegetables (U = 61.5, P = 0.002). We discuss the ramifications of exposure to a globalised food system and how experiences at school can nurture children's understanding of food. We interpret the outcomes of the different pedagogical approaches and unravel the importance of ‘place' in a child's food experience. The evidence presented suggests that children’s food knowledge could improve if food education is bolstered with experiential and place-responsive pedagogies.

Every Lesson Needs a Gandalf: How Interactive Storytelling can Enhance the Collaborative Learning Experience!

Every teacher aspires to create the ideal lesson! You want to convey the knowledge and skills in the best possible way while also keeping learners motivated. In our design case, we focus on the complex skill of collaborative problem-solving (CPS). In today’s complex world, the acquisition of CPS competencies is considered an important learning goal in education. However, there is limited knowledge on how to teach and assess CPS competencies. In addition to tackling these challenges, we search for new ways of interactive storytelling to implement in the learning materials. Our main design challenge was how to design a learning experience that encourages CPS. To address these challenges, we started in 2020 the project titled Supporting TEamwork in AMbient learning Spaces (STEAMS). In this project, we designed the EDUbox Teamwork, a four-hour learning activity for children between the ages of 10 to 14. In this paper, we describe the iterative process of designing the materials to learn about CPS (i.e., CPS as a learning goal) by doing CPS (i.e., CPS as a method), enhanced by interactive storytelling, for which the design-based research approach was used. The design team consisted of a diverse group of educational researchers, computer scientists, instructional graphical designers, digital storytellers, and teachers. Given the strong collaboration between a research group specialized in computer-supported collaborative learning and specialists in digital storytelling, our design case incorporated insights from both parties. The learning activity was piloted both in in-vivo and in-vitro contexts in collaboration with at least 400 students.

Exploring Teacher-Student Interaction in a Synchronous Online Modality: A Critical Discourse Analysis

The rise of online education, prompted by challenges posed by the COVID-19 pandemic, has revolutionized the education landscape and challenged the traditional norms and communications that have characterized communication in the education sector. This research, drawing on the interplay between Critical Discourse Analysis and Sinclair’s IRF model, peers into how language power is played out in cyberspaces of a virtual classroom. The study traces language’s hidden yet palpable influence in shaping power relations through an examination of various online engagements. Findings from the study show that teachers use rhetorical questions and affirmations to guide discussions and retain power based on Sinclair’s IRF model. On the other hand, Wodak’s CDA framework brings out the hidden power dimensions that lie unnoticed in language, which seems inclusive but seeks control. The outcome confirms how language influences power dynamics when teachers use linguistic strategies to shape narratives and dictate conversations. The study reveals strategies instructors utilize in General Education courses at Camarines Sur Polytechnic Colleges, Philippines. While appearing democratic, rhetorical questions and affirmations maintain the teacher’s authority as they help create an illusion of equality. The paper emphasizes the necessity of understanding language-power interplay in online education and advocates for including a course component devoted to language and power dynamics in teacher professional development programs. This awareness creates inclusive environments in online learning spaces where various voices are recognized, encouraging shared knowledge development. This research illuminates the intricate nature of language and power, laying the foundation for informed online learning teaching practices.

Meta-state–space learning: An identification approach for stochastic dynamical systems

Available methods for identification of stochastic dynamical systems from input–output data generally impose restricting structural assumptions on either the noise structure in the data-generating system or the possible state probability distributions. In this paper, we introduce a novel identification method of such systems, which results in a dynamical model that is able to produce the time-varying output distribution accurately without taking restrictive assumptions on the data-generating process. The method is formulated by first deriving a novel and exact representation of a wide class of nonlinear stochastic systems in a so-called meta-state–space form, where the meta-state can be interpreted as a parameter vector of a state probability function space parameterization. As the resulting representation of the meta-state dynamics is deterministic, we can capture the stochastic system based on a deterministic model, which is highly attractive for identification. The meta-state–space representation often involves unknown and heavily nonlinear functions, hence, we propose an artificial neural network (ANN)-based identification method capable of efficiently learning nonlinear meta-state–space models. We demonstrate that the proposed identification method can obtain models with a log-likelihood close to the theoretical limit even for highly nonlinear, highly stochastic systems.

Learning from demonstration for 7-DOF anthropomorphic manipulators without offset via analytical inverse kinematics

Learning from demonstration (LfD) has been widely studied as a convenient method for robot learning. In the LfD paradigm for redundant manipulators, the reproduced trajectories ought to be similar to human demonstrations in both task and joint space. Despite many advancements achieved in the context of learning in task space, the solutions for generating similar reproductions in joint space are still largely open. In this paper, a novel non-parametric LfD framework for 7-DOF anthropomorphic manipulators with high computational efficiency is proposed. The proposed method leverages redundancy resolution and kernel-based approaches to formulate an efficient model characterized by a limited set of open parameters. Experiments were conducted to evaluate the performance of the proposed method and compare it with the commonly used ‘LfD+IK’ solution. The results indicated that the proposed method behaves much better in terms of the similarity between the demonstration and reproduction with high computing efficiency. As the proposed method can learn from human demonstrations effectively in both task and joint space, it has the potential to significantly enhance human–robot collaboration, streamline assembly line processes, or improving robot learning. An important future challenge will be extending the proposed method for general-purpose redundant manipulators and considering task constraints to perform complex tasks.

Visual perceptual learning is enhanced by training in the illusory far space.

Visual objects in the peripersonal space (PPS), are perceived faster than farther ones, appearing in the extrapersonal space (EPS). This shows preferential processing for visual stimuli near our body. Such an advantage should favor visual perceptual learning occurring near, as compared to far from observers, but opposite evidence has been recently provided from online testing protocols, showing larger perceptual learning in the far space. Here, we ran two laboratory-based experiments investigating whether visual training in PPS and EPS has different effects. We used the horizontal Ponzo Illusion to create a lateralized depth perspective while participants completed a visual search task in which they reported whether or not a specific target object orientation (e.g., a triangle pointing upward) was present amongst distractors. This task was completed before and after a training phase in either the (illusory) near or far space for one hour. In Experiment 1, the near space was in the left hemispace, whereas in Experiment 2 it was in the right. Results showed that, in both experiments, participants were more accurate after training in the far space, whereas training in the near space led to either improvement in the far space (Exp. 1), or no change (Exp. 2). Moreover, we found a larger visual perceptual learning when stimuli were presented in the left compared to the right hemispace. Differently from visual processing, visual perceptual learning is more effective in the far space. We propose that depth is a key dimension that can be used to improve human visual learning.

Information-enhanced deep graph clustering network

Graph clustering is a significant task in complex network research. Deep graph clustering aims to uncover the potential community structure in graph data using the powerful feature extraction capability of deep learning, garnering much attention in recent decades. However, existing graph clustering methods fall short in fully utilizing available information, particularly in effectively fusing structural and attribute information, as well as utilizing coarse-grained data. Consequently, learned node representations remain limited, leading to suboptimal clustering results. To address these challenges, we propose Information-Enhanced Deep Graph Clustering Network (IEDGCN) for unsupervised attribute graphs. IEDGCN introduces key components to enhance information utilization and improve clustering performance. Firstly, we design a new higher-order neighborhood-weighted attribute matrix, effectively integrating higher-order neighborhood information with attributes. Secondly, a graph generation model guides the learning of the structural feature space more effectively. Additionally, IEDGCN captures more coarse-grained information by utilizing community and higher-order neighborhood features to refine clustering results. Finally, the proposed method is uniformly guided through a jointly supervised strategy for representation learning and cluster assignment. Experimental results on different benchmark datasets demonstrate the effectiveness of IEDGCN compared to state-of-the-art methods, emphasizing the importance of information enhancement for graph clustering.

A Multi-Objective Method Based on Tag Eigenvalues Is Used to Predict the Supply Chain for Online Retailers

E-commerce has grown quickly in recent years thanks to advancements in Internet and information technologies. For the majority of consumers, online shopping has emerged as a primary mode of shopping. However, it has become more challenging for businesses to satisfy consumer demand due to their increasingly individualized wants. To address the need for customized products with numerous kinds and small quantities, businesses must rebuild their supply chain systems to increase their efficiency and adaptability. The SI-LSF technique, which employs boosting learning in the target-relative feature space to lower the prediction error and enhance the algorithm's capacity to handle input-output interactions, is validated in this study using a genuine industrial dataset. The study successfully identifies the relationship between sales and sales as well as target-specific features by applying the multi-objective regression integration algorithm based on label-specific features to a real-world supply chain demand scenario.

Nursing Students’ Perspectives on Caring Pedagogical Relationship Within the Context of Simulation: A Phenomenological Inquiry

Abstract:This phenomenological research aimed to describe and understand nursing students’ meaning of the lived experience of a caring pedagogical relationship within the context of high-fidelity clinical simulation. Being inspired by a relational emancipatory pedagogy perspective, our findings highlighted an authentic engagement of educators as the cornerstone to fostering a safe, caring, and inclusive learning space, allowing them to co-create a humanistic collaboration with students to transform their learning process. Innovative and various avenues arising from our findings have been suggested in different domains of nursing practice to promote students’ learning and develop a caring inclusive learning community within simulation centers.

Enhanced Indoor Air Quality Dashboard Framework and Index for Higher Educational Institutions

This research proposes a 10-step methodology for developing an enhanced IAQ dashboard and classroom index (CI) in higher educational facilities located in arid environments. The identified parameters of the enhanced IAQ dashboard–inspired by the pandemic experience, result from the literature review and the outcome of two electronic surveys of (52) respondents, including health professionals and facility management experts. On the other hand, the indicators included in the CI are based on (80) occupant survey responses, including parameters related to IAQ, Indoor Environmental Quality (IEQ), and thermal comfort, amongst other classroom operative considerations. The CI is further tested in four learning spaces at the American University in Cairo, Egypt. The main contribution of this research is to suggest a conceptual visualization of the dashboard and a practical classroom index that integrates a representative number of contextual indicators to recommend optimal IAQ scenarios for a given educational facility. This study concludes by highlighting several key findings: (1) both qualitative and quantitative metrics are necessary to capture indoor air quality-related parameters accurately; (2) tailoring the dashboard as well as the CI to specific contexts enhances its applicability across diverse locations; and finally, (3) the IAQ dashboard and CI offer flexibility for ad-hoc applications.

Exploring Exciting Ways to Teach: A Comprehensive Analysis of Modern Teaching Methods

This study explores the evolving landscape of education by investigating emerging teaching methods and analyzing their benefits and challenges in the context of modern education. Employing a documentary-analysis methodology, the research sheds light on contemporary approaches such as cooperative learning, project-based learning, spaced learning, flipped classrooms, tactile learning, and blended learning. These methods are identified as pivotal trends in the current educational era. The researcher examines both the positive and negative aspects of these approaches, providing valuable insights for educators. The study concludes that the modern teaching approach is predominantly experience-based and learner-centered, diverging from the teacher-centered approach. This research equips educators with significant knowledge to make informed decisions in selecting appropriate teaching methods for effective learning.

Digital Wellness Framework for Online Learning

The ever-changing digital context, digital habits and pressures, demands and practices, often contribute to online learners experiencing burnout, stress, fatigue, sleep deprivation, cognitive overwhelm, and work-life imbalance, just to mention a few issues identified in literature. With the rise of online learning offerings, an increasing number of educators across diverse contexts and disciplines are faced with questions pertaining to the optimal experience and design for online learning. Current research has highlighted both positive and negative impacts of teaching and learning in the digital space. This online learning design debate has identified a need for practices that contribute to the holistic wellbeing of learners rather than merely cognitive outcomes. There is a need for an evidence-based pedagogical framework centred on wellbeing that enables the creation of learning “by design”. This research, applying secondary data analysis and a mindfulness-informed lens, results in such a framework, i.e., the DW-FOLD: Digital Wellness Framework for Online Learning – to guide intentional use of technology and online learning pedagogical principles that ensure active and meaningful learning while using technology for the good of all learners.

A Qualitative Study on University Students’ Restorative Experience of the Library Space Environment

Currently, university students are facing high levels of stress and negativity. Creating a campus learning environment that promotes students’ physical and mental health and well-being has become a key focus of current research. There is now a wealth of research proving that the natural attributes of learning spaces are highly restorative for university students, both physically and mentally. In order to comprehensively clarify the characteristics of the library spatial environment that has a restorative effect on university students, this paper conducted semi-structured interviews with 50 university students and thematically analyzed the data obtained from these interviews. Ultimately, we identified five key themes: (1) Being away: students believe that studying alone is a way to escape social pressures, immersing themselves in their studies helps them forget everything around them, and the view from the window creates a sense of detachment; (2) Extent: students perceive the design of the library space as rich and orderly; (3) Fascination: many elements of the library spatial environment appeal to them and make them feel energized. The overall atmosphere motivates them to engage more actively in their learning; (4) Compatibility: the library spatial environment provides a variety of facilities that meet their learning needs, along with various types of spaces catering for different behaviors; and (5) Biophilia: Many interviewees mentioned the benefits of natural environments and timber, as well as their preference for spaces with a sufficient sense of enclosure and the ability to observe others. The first four themes were found to be closely related to the Attention Restorative Theory during the course of the study, while the last theme was mentioned by most of the students. The results of this study prove that library spaces can be described as restorative environments in the same way as natural environments, with both physical and mental restorative effects. Furthermore, the results of this study can also provide certain ideas for the design and renovation of the internal spatial environment of university libraries.

Creating Spaces of Engagement: Exploring High School Youth’s Voices in Reshaping the Social Justice Curriculum

The current structure of formal education makes it difficult for teachers and students to hold meaningful conversations to support high-school youth’s meaning-making of critical social-justice issues. This paper presents data on three high-school youth’s knowledge and experiences with social justice issues during the pandemic. Specifically, the paper aims to explore how youth construct knowledge and counter dominant discourses through utilizing informal learning spaces, such as social media platforms, peer and family conversations, as well as personal encounters. In addition, and more importantly, an exploration of how formal education can incorporate social-justice issues into the curriculum is considered. The analysis of these high school youth’s interview conversations presents their diverging needs to learn about social-justice topics in both formal and informal learning contexts. The data also illustrates the power of their voices in a way that could inform future curriculum development. Discussions and implications highlight the possibility of creating such ethical spaces in formal education to engage in social-justice topics.